[1]} develop a two stage methodology where Tuberculosis infected lung CT images are analyzed in a disentangled manner and produce counterfactual images depicting how the patient would look like if they were healthy. Contrary to other approaches the authors use a DAG to represent the image generation process and parametrize it using a neural network such that sampling and use of it is straightforward for the counterfactual generation step.
[2]} developed a normalizing flow model to perform the abduction step in an abduction-action-prediction counterfactual inference task and are able to generate plausible brain MRI volumes. Reynaud et al. in   [3]} assume a different approach and develop a generative model based on Deep Twin Networks [4]}. Performing counterfactual inference in the latent space embeddings, the authors are able to generate realistic Ultrasound Videos with different Left Ventricle Ejection Fractions. Their approach is similar to [5]} in the sense that a GAN is used to provide a training signal for the generated, counterfactual samples.
